A method for magnetic resonance electrical property imaging of the human liver

Abstract

The present application relates to the technical field of magnetic resonance imaging, in particular to a magnetic resonance human liver electrical property imaging method, comprising the following steps: step S1, acquiring a magnetic resonance image; step S2, calculating fat content distribution; step S3, segmenting a human liver tissue image; step S4, establishing a function model of human liver fat content and electrical property; and step S5, calculating human liver electrical property distribution. The present application provides a magnetic resonance human liver electrical property imaging method, which obtains a high-accuracy liver electrical property distribution image through a reasonable fat content acquisition route and based on the relationship between fat content and electrical property, and is beneficial to clinical liver examination and diagnosis, and solves the problem of reduced imaging accuracy of the human liver in traditional electrical property technology due to the principle defect.

Description

Technical Field

[0001] This invention relates to the field of magnetic resonance imaging technology, specifically to a magnetic resonance imaging method for the electrical properties of the human liver. Background Technology

[0002] Electrical properties of human tissues, including conductivity and relative permittivity. Numerous studies have confirmed that these values ​​change when tissue becomes diseased, often before morphological changes in tumors. Therefore, they are suitable as biomarkers to provide valuable information for the early diagnosis and identification of tumors.

[0003] In recent years, magnetic resonance imaging (MRI) based on the electrical properties of human tissues has been proposed, mainly divided into radio frequency emission field-based electrical property imaging (B1-EPT) and tissue water content-based electrical property imaging (w-EPT).

[0004] B1-EPT is a non-invasive imaging technique for measuring the electrical properties of human tissue by detecting radio frequency emission fields using magnetic resonance imaging and based on Maxwell's algorithm. This technique requires highly accurate amplitude and phase data of the radio frequency emission field and amplifies noise interference during the calculation process.

[0005] w-EPT first performs magnetic resonance imaging on the tested tissue, then uses water content imaging technology to determine the water content distribution within the tissue, and finally calculates the electrical property distribution based on the "water content-electrical property" functional relationship. This technique, in principle, avoids the need for a radio frequency transmission field and the interference of noise factors on the calculation. However, the tissues currently tested for this technique are mostly brain tissues with high water content. Therefore, the above method has certain limitations for imaging the electrical properties of the human liver. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a magnetic resonance imaging method for the electrical properties of the human liver. This method avoids the fundamental flaws in traditional electrical property techniques that lead to reduced accuracy in human liver imaging.

[0007] The technical problem to be solved by this invention is achieved by the following technical solution:

[0008] A magnetic resonance imaging method for electrical properties of the human liver includes the following steps:

[0009] Step S1: Acquire magnetic resonance images:

[0010] Using Dixon technology, a magnetic resonance imaging (MRI) scanner is used to scan the abdomen of the subject to obtain Dixon water and fat images;

[0011] Step S2: Calculate the fat content distribution:

[0012] Based on the amplitude information in the Dixon water image and fat image obtained in step S1, the fat content in various parts of the abdominal region of the human body under test is calculated.

[0013] Step S3: Segmenting human liver tissue image:

[0014] The liver tissue was segmented from the acquired image of human abdominal fat content.

[0015] Step S4: Establish a functional model of the relationship between fat content and electrical properties in the human liver:

[0016] σ = -0.6153 × f + 0.5054

[0017] ε=-98.8487×f+84.2449

[0018] In the formula, σ represents conductivity and ε represents dielectric constant;

[0019] Step S5: Calculate the distribution of electrical properties of the human liver:

[0020] The fat content distribution data calculated in step S2 is input into the function model established in step S4 to obtain the distribution of human liver electrical properties.

[0021] Preferably, the field strength requirement of the magnetic resonance imaging instrument in step S1 is 1.5T, 3T, or 7T.

[0022] Preferably, the calculation formula in step S2 is as follows:

[0023] f = f / (w + f)

[0024] In the formula, f represents the amplitude of the Dixon fat signal, and w represents the amplitude of the Dixon water signal.

[0025] Preferably, the specific process in step S3 is as follows:

[0026] By referencing MRI images of the human liver, the outline of the liver tissue was constructed using 3Dslicer software, and regions belonging only to the liver tissue were delineated, ensuring that the tested tissue was focused solely on the liver.

[0027] Preferably, the specific process in step S4 is as follows:

[0028] By simulating real human liver tissue using phantoms with different fat contents, the conductivity and dielectric constant of the liver were obtained using the open coaxial method, and a functional model of the relationship between fat content and electrical properties of the human liver was obtained by fitting.

[0029] The beneficial effects of this invention are:

[0030] This invention provides a magnetic resonance imaging method for the electrical properties of the human liver. By using a reasonable route to obtain fat content and based on the relationship between "fat content and electrical properties", it obtains a highly accurate image of the distribution of liver electrical properties, which is beneficial for clinical liver examination and diagnosis. It solves the problem of reduced accuracy in human liver imaging caused by the fundamental defects in traditional electrical property technology. Attached Figure Description

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0032] Figure 1 This is a flowchart of the present invention;

[0033] Figure 2 This is an image of abdominal fluid obtained by magnetic resonance imaging in this invention;

[0034] Figure 3 This refers to a fat image obtained by magnetic resonance scanning in this invention;

[0035] Figure 4 This is an image of abdominal fat content calculated and obtained in this invention;

[0036] Figure 5 This is the fitting function model for fat content at 1.5T in this invention;

[0037] Figure 6 This is a segmentation map of the electrical properties of liver tissue calculated in this invention. Detailed Implementation

[0038] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] A magnetic resonance imaging method for the electrical properties of the human liver is disclosed, which utilizes complete magnetic resonance scan data. In this embodiment, the input data is magnetic resonance scan data of a real human abdomen, a 16-channel array coil is used as the radio frequency coil, the measurement frequency is 64MHz, and the Dixon sequence is selected as the scanning sequence.

[0040] like Figure 1 As shown, the steps include: step S1 acquiring magnetic resonance images, step S2 calculating fat content distribution, step S3 segmenting human liver tissue images, step S4 establishing a functional model of human liver fat content and electrical properties, and step S5 calculating the distribution of human liver electrical properties.

[0041] The specific steps are as follows:

[0042] Step S1: Obtain magnetic resonance images.

[0043] Using Dixon technology, a magnetic resonance imaging (MRI) scanner is used to scan the abdomen of the subject to acquire Dixon water and fat images. The required field strength for the MRI scanner is 1.5T, 3T, and 7T.

[0044] In this embodiment, a 23-year-old adult male was selected for testing, and magnetic resonance imaging (MRI) images were acquired using a 1.5T MRI scanner. A 16-channel array coil was used for the radiofrequency (RF) coil, with a frequency set to 64MHz, and the scan site was the abdomen. The clinically common Dixon sequence was selected for the MRI scan.

[0045] The signal calculation parameters in step S1 are set as follows: repetition time TR is set to 140 ms, longitudinal relaxation time TE is set to 2.9 ms, and flip angle θ is set to 45°. The Dixon images of human liver water and fat obtained in this step are shown below. Figure 2 and Figure 3 As shown.

[0046] Step S2: Calculate the fat content distribution.

[0047] In this embodiment, fat content is calculated based on Dixon images of the abdomen of a real adult male. Since magnetic resonance Dixon scans can acquire both water and fat images of the target area, the fat content distribution in the abdomen can be calculated by substituting the amplitude data of the water and fat images into the formula for fat content calculation. The formula is as follows:

[0048] f = f / (w + f)

[0049] In the formula, f represents the amplitude of the Dixon fat signal, and w represents the amplitude of the Dixon water signal.

[0050] The abdominal fat percentage image obtained in this step is as follows: Figure 4 As shown.

[0051] Step S3: Segment the human liver tissue image.

[0052] The liver tissue segmentation method used in this example is performed using 3Dslicer software. The obtained MRI abdominal image is segmented with reference to anatomical atlas, so that the subsequent calculation area is only for liver tissue.

[0053] Step S4: Establish a functional model of the relationship between fat content and electrical properties in the human liver.

[0054] This function model is a general model for the liver. It simulates phantoms of real human livers with varying fat content, calculates their conductivity and dielectric constant using the open-ended coaxial method, and finally fits the "fat content-electrical properties" relationship to derive the function model. The function model is as follows:

[0055] σ = -0.6153 × f + 0.5054

[0056] ε=-98.8487×f+84.2449

[0057] In the formula, σ represents conductivity and ε represents dielectric constant.

[0058] The results show that at electric field strengths of 1.5T, 3T, and 7T, the fitting relationships all follow a linear distribution and the fitting results are superior. Furthermore, these fitting results have been verified with values ​​from authoritative literature, demonstrating their feasibility. The fitting of the "fat content-electrical properties" relationship at 1.5T in this implementation design is as follows: Figure 5 As shown.

[0059] Step S5: Calculate the distribution of electrical properties of the human liver.

[0060] By inputting the fat content distribution data calculated in step S2 into the function model established in step S4, the final conductivity and electrical property imaging of the human liver at 1.5T can be obtained, such as... Figure 6 As shown.

[0061] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely prisms of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A magnetic resonance imaging method for the electrical properties of the human liver, characterized in that: Includes the following steps: Step S1: Acquire magnetic resonance images: Using Dixon technology, a magnetic resonance imaging (MRI) scanner is used to scan the abdomen of the subject to obtain Dixon water and fat images; Step S2: Calculate the fat content distribution: Based on the amplitude information in the Dixon water image and fat image obtained in step S1, the fat content in various parts of the abdominal region of the human body under test is calculated. Step S3: Segmenting human liver tissue image: The liver tissue was segmented from the acquired image of human abdominal fat content. Step S4: Establish a functional model of the relationship between fat content and electrical properties in the human liver: ， ， In the formula, Represents electrical conductivity. Represents the dielectric constant. This represents Dixon's fat content; Step S5: Calculate the distribution of electrical properties of the human liver: The fat content distribution data is input into the function model established in step S4 to obtain the distribution of human liver electrical properties.

2. The magnetic resonance imaging method for electrical properties of the human liver according to claim 1, characterized in that: In step S1, the field strength requirements for the magnetic resonance imaging (MRI) instrument are 1.5T, 3T, and 7T.

3. The magnetic resonance imaging method for the electrical properties of the human liver according to claim 1, characterized in that: The calculation formula in step S2 is as follows: ； In the formula, This represents Dixon's fat content. Represents the amplitude of the Dixon fat signal. This represents the amplitude of the Dixon water signal.

4. The magnetic resonance imaging method for electrical properties of the human liver according to claim 1, characterized in that: The specific process in step S3 is as follows: By referencing MRI images of the human liver, the outline of the liver tissue was constructed using 3Dslicer software, and regions belonging only to the liver tissue were delineated, ensuring that the tested tissue was focused solely on the liver.

5. The magnetic resonance imaging method for electrical properties of the human liver according to claim 1, characterized in that: The specific process in step S4 is as follows: By simulating real human liver tissue using phantoms with different fat contents, the conductivity and dielectric constant of the liver were obtained using the open coaxial method, and a functional model of the relationship between fat content and electrical properties of the human liver was obtained by fitting.

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